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Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

[+] Author Affiliations
Zuhe Li

Northwestern Polytechnical University, School of Electronics and Information, Xi’an, China

Zhengzhou University of Light Industry, School of Computer and Communication Engineering, Zhengzhou, China

Yangyu Fan

Northwestern Polytechnical University, School of Electronics and Information, Xi’an, China

Weihua Liu

Chinese Academy of Sciences, Xi’an Institute of Optics and Precision Mechanics, Xi’an, China

Zeqi Yu, Fengqin Wang

Zhengzhou University of Light Industry, School of Computer and Communication Engineering, Zhengzhou, China

J. Electron. Imaging. 26(1), 013022 (Feb 23, 2017). doi:10.1117/1.JEI.26.1.013022
History: Received October 5, 2016; Accepted February 7, 2017
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Abstract.  We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder’s hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

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Citation

Zuhe Li ; Yangyu Fan ; Weihua Liu ; Zeqi Yu and Fengqin Wang
"Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection", J. Electron. Imaging. 26(1), 013022 (Feb 23, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.1.013022


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